L Bottou, J Peters, J Quiñonero-Candela, D X Charles, D M Chickering, E Portugaly, D Ray, P Simard, E Snelson. Counterfactual reasoning and learning systems: the example of computational advertising. Journal of Machine Learning Research, 2013, 14(1): 3207–3260
2
K Hofmann, L Li, F Radlinski. Online evaluation for information retrieval. Foundations and Trends in Information Retrieval, 2016, 10(1): 1–117 https://doi.org/10.1561/1500000051
3
L Li, W Chu, J Langford, R E Schapire. A contextual-bandit approach to personalized news article recommendation. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 661–670 https://doi.org/10.1145/1772690.1772758
4
M Dudík, J Langford, L Li. Doubly robust policy evaluation and learning. In: Proceedings of the 28th International Conference on Machine Learning. 2011, 1097–1104
5
A Swaminathan, T Joachims. The selfnormalized estimator for counterfactual learning. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015, 3231–3239
6
Y X Wang, A Agarwal, M Dudík. Optimal and adaptive off-policy evaluation in contextual bandits. In: Proceedings of the 34th International Conference on Machine Learning. 2017, 3589–3597
7
N Jiang, L Li. Doubly robust off-policy evaluation for reinforcement learning. In: Proceedings of the 33rd International Conference on Machine Learning. 2016, 652–661
8
L Li, R Munos, C Szepesvári. Toward minimax off-policy value estimation. In: Proceedings of the 18th International Conference on Artificial Intelligence and Statistics. 2015, 608–616
9
D Precup, R S Sutton, S P Singh. Eligibility traces for off-policy policy evaluation. In: Proceedings of the 17th International Conference on Machine Learning. 2000, 759–766
10
Q Liu, L Li, Z Tang, D Zhou. Breaking the curse of horizon: infinitehorizon off-policy estimation. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2018, 5361–5371
